As shown in the Figure REF , both slices based methods we compare show topological errors despite the input contours are not too sparse. For example, Bermano's method show disconnections almost in all models. Also, there is a problem with handling branching cases in kidney and chair models. Thus requiring more contours to generate thin parts of the shape. Similar disconnection problems are present in the results generated by Zou's method, for chair and table models. While this method handles branching issues in most cases, it adds some additional parts to the shape, in brain and kidney models. We also generate results with point cloud based (Poisson surface reconstruction) method [1]} passing all points in the slices as input. As illustrated in the Figure REF , this method generates mediocre outputs for smooth surfaces (e.g. brain, kidney, lung). However, for flat surfaces like chair and table it fails. This is mainly related with the accuracy of point normal estimation for input points. Additionally, Figure REF  shows results of baseline method without using projections images. However, this method assumes that the input slices are connected. Results generated with this method show some additional parts which not exist in the ground truth mesh. Quantitative results corresponding to this experiment, which includes IoU and Chamfer distance (CD) metrics, is summarized in Table REF . Shape reconstruction from a few slices is shown in the Figure REF . For this type of input slices based surface reconstruction methods, Bermano's method and Zou's method with genus did not output a connected shape. The latter only handles partially when one of the views (as an image) was presented. This also indicates the importance of using orthographic projections.
